Introduction to ‘parallelPlot’

library(parallelPlot)

Basic usage (dataset uses factor type)

parallelPlot(iris)

‘species’ column is of factor type and has box representation for its categories.

refColumnDim argument (referenced column is categorical)

parallelPlot(iris, refColumnDim = "Species")

Each trace has a color depending of its ‘species’ value.

categoricalCS argument

parallelPlot(iris, refColumnDim = "Species", categoricalCS = "Set1")

Colors used for categories are not the same as previously (supported values: Category10, Accent, Dark2, Paired, Set1).

refColumnDim argument (referenced column is continuous)

parallelPlot(iris, refColumnDim = "Sepal.Length")

Each trace has a color depending of its ‘Sepal.Length’ value.

continuousCS argument

parallelPlot(iris, refColumnDim = "Sepal.Length", continuousCS = "YlOrRd")

Colors used for traces are not the same as previously (supported values: Blues, RdBu, YlGnBu, YlOrRd, Reds).

Basic usage (dataset doesn’t use factor type)

parallelPlot(mtcars)

Several columns are of numerical type but should be of factor type (for example ‘cyl’).

categorical argument

categorical <- list(NULL, c(4, 6, 8), NULL, NULL, NULL, NULL, NULL, c(0, 1), c(0, 1), 3:5, 1:8)
parallelPlot(mtcars, categorical = categorical, refColumnDim = "cyl")

‘cyl’ and four last columns have a box representation for its categories.

inputColumns argument

categorical <- list(NULL, c(4, 6, 8), NULL, NULL, NULL, NULL, NULL, c(0, 1), c(0, 1), 3:5, 1:8)
inputColumns <- c(FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE)
parallelPlot(mtcars, categorical = categorical, inputColumns = inputColumns, refColumnDim = "cyl")

The column name is blue for outputs and green for inputs (in shiny mode, inputs can be edited).

histoVisibility argument

histoVisibility <- rep(TRUE, ncol(iris))
parallelPlot(iris, histoVisibility = histoVisibility)

An histogram is displayed for each column.

invertedAxes argument

invertedAxes <- rep(FALSE, ncol(iris))
invertedAxes[2] <- TRUE
parallelPlot(iris, invertedAxes = invertedAxes)

Axe of second column is inverted.

cutoffs argument

histoVisibility <- rep(TRUE, ncol(iris))
cutoffs <- list(list(c(6, 7)), NULL, NULL, NULL, c("virginica", "setosa"))
parallelPlot(iris, histoVisibility = histoVisibility, cutoffs = cutoffs)

Traces which are not kept by cutoffs are greyed; an histogram is displayed considering only kept traces.

refRowIndex argument

parallelPlot(iris, refRowIndex = 1)

Axes are shifted vertically in such a way that first trace of the dataset looks horizontal.

rotateTitle argument

parallelPlot(iris, refColumnDim = "Species", rotateTitle = TRUE)

Column names are rotated (can be useful for long column names).

columnLabels argument

columnLabels <- gsub("\\.", "<br>", colnames(iris))
parallelPlot(iris, refColumnDim = "Species", columnLabels = columnLabels)

Given names are displayed in place of column names found in dataset; <br> is used to insert line breaks.

cssRules argument

parallelPlot(iris, cssRules = list(
    "svg" = "background: white", # Set background of plot to white
    ".tick text" = c("fill: red", "font-size: 1.8em") # Set text of axes ticks red and greater
))

Apply CSS to the plot. CSS is a simple way to describe how elements on a web page should be displayed (position, colour, size, etc.). You can learn the basics at W3Schools. You can learn how to examine and edit css at MDN Web Docs for Firexox or Chrome devtools for Chrome.

sliderPosition argument

parallelPlot(iris, sliderPosition = list(
  dimCount = 3, # Number of columns to show
  startingDimIndex = 2 # Index of first shown column
))
# Visible columns starts at second column and three columns are represented.

Set initial position of slider, specifying which columns interval is visible.

controlWidgets argument

parallelPlot(iris, refColumnDim = "Species", controlWidgets = TRUE)

Some widgets are available to control the plot.